Metadata-Version: 2.1
Name: fastlmm
Version: 0.6.0
Summary: Fast GWAS
Home-page: https://fastlmm.github.io/
Author: FaST-LMM Team
Author-email: fastlmm-dev@python.org
License: Apache 2.0
Project-URL: Bug Tracker, https://github.com/fastlmm/FaST-LMM/issues
Project-URL: Documentation, http://fastlmm.github.io/FaST-LMM
Project-URL: Source Code, https://github.com/fastlmm/FaST-LMM
Description: FaST-LMM
        =================================
        
        FaST-LMM, which stands for Factored Spectrally Transformed Linear Mixed Models, is a program for performing 
        genome-wide association studies (GWAS) on datasets of all sizes, up to one millions samples.
        
        This release contains the following features, each illustrated with an IPython notebook.
        
        * Core FaST-LMM ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html)
        
        Improvements:
        
        * New features for single_snp (including multiple phenotype support) and epistasis (including reporting beta and using pre-computed eigenvalue decompositions) ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/fastlmm2021.ipynb))  -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html)
        * Ludicrous-Speed GWAS ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/SingleSnpScale.ipynb)) -- [Kadie and Heckerman, *bioRxiv* 2018](https://www.biorxiv.org/content/10.1101/154682v2)
        * Heritability with Spatial Correction ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/heritability_si.ipynb)), [Heckerman *et al.*, *PNAS* 2016](http://www.pnas.org/content/113/27/7377.abstract)
        * Two Kernels ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Widmer *et al.*, *Scientific Reports* 2014](http://www.nature.com/srep/2014/141112/srep06874/full/srep06874.html)
        * Set Analysis ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Bioinformatics* 2014](http://bioinformatics.oxfordjournals.org/content/early/2014/09/07/bioinformatics.btu504)
        * Epistasis ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Scientific Reports,* 2013](http://www.nature.com/srep/2013/130122/srep01099/full/srep01099.html)
        * Prediction ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html)
        
        *A C++ version, which is generally less functional, is available. See http://fastlmm.github.io/.*
        
        Quick install:
        =================================
        
        `pip install fastlmm`
        
        For best performance, be sure your Python distribution includes a fast version of NumPy. We use Anaconda's [Miniconda](https://docs.conda.io/en/latest/miniconda.html).
        
        Documentation
        =================================
        
        * IPython Notebooks:
        	* [Core, Epistasis, Set Analysis, Two Kernels](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)
            * [Multiple-Phenotype GWAS and related features](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/fastlmm2021.ipynb)
        	* [Heritability with Spatial Correction](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/heritability_si.ipynb)
        	* [Ludicrous-Speed GWAS](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/SingleSnpScale.ipynb)
        * [Main Documentation](http://fastlmm.github.io/FaST-LMM/)
        * [Project Home and Full Annotated Bibliography](https://fastlmm.github.io/)
        
        
        Code
        =================================
        * [PyPi](https://pypi.org/project/fastlmm/)
        * [GitHub](https://github.com/fastlmm/FaST-LMM)
        
        Contacts
        =================================
        
        * Email the developers at fastlmm-dev@python.org.
        * [Join](mailto:fastlmm-user-join@python.org?subject=Subscribe) the user discussion and announcement list (or use [web sign up](https://mail.python.org/mailman3/lists/fastlmm-user.python.org)).
        * [Open an issue](https://github.com/fastlmm/FaST-LMM/issues) on GitHub.
        
Keywords: gwas bioinformatics LMMs MLMs linear mixed models genomics genetics python
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown
